Intelligent vehicle repair estimating techniques include an image processing component that extracts image attributes from one or more images of a damaged vehicle, and utilizes the attributes to predict an initial set of parts that are globally-identified. Based on a jurisdiction associated with the damaged vehicle, the initial set of parts is transformed into a set of jurisdictionally-based repairs (e.g., parts, labor operations, time intervals, costs, etc.), which may be included in a draft vehicle repair estimate. An estimate refinement component iteratively modifies/refines the draft estimate using a machine-only loop nested within a larger human-machine loop, where system-generated modifications are incrementally incorporated into the draft within the smaller loop, and user-generated modifications are incrementally incorporated into the draft within the larger loop. User-facing draft estimates may be of a WYSIWYG format, and draft estimate contents and/or guidance annotations are updated, via the nested loops, in-line upon each individual/unitary user modification.
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2. The method of claim 1, wherein determining the jurisdictionally-based set of vehicle parts based on the set of global identifiers of the initial set of parts includes filtering parts indicated in a vehicle parts database based on at least one of: a characteristic of the damaged vehicle, one or more requirements included in the set of jurisdictional requirements, respective levels of complexity of one or more parts, or a frequency of part use within the jurisdiction corresponding to the damaged vehicle.
3. The method of claim 1, further comprising determining whether each jurisdictionally-based vehicle part included in at least a portion of the jurisdictionally-based set of vehicle parts is to be repaired or replaced, and wherein the predicted set of labor operations corresponds to whether each jurisdictionally-based part included in the at least the portion of the jurisdictionally-based set of vehicle parts is to be repaired or replaced.
4. The method of claim 3, wherein determining whether each jurisdictionally-based vehicle part included in the at least the portion of the jurisdictionally-based set of vehicle parts is to be repaired or replaced includes utilizing the trained machine learning context mapping model to predict whether each jurisdictionally-based vehicle part included in the at least the portion of the jurisdictionally-based set of vehicle parts is to be repaired or replaced.
5. The method of claim 1, wherein utilizing the trained machine learning context mapping module to predict the set of labor operations needed to repair the damaged vehicle includes utilizing the trained machine learning context mapping module to predict respective time intervals corresponding to the set of labor operations in conjunction with predicting the set of labor operations.
6. The method of claim 1, wherein the set of jurisdictional requirements includes one or more jurisdictional requirements corresponding to at least one of: hazardous waste disposal, materials which are illegal to utilize in vehicle repair, emissions, a country of manufacture, an insurance regulation, a tax, or a fee.
7. The method of claim 1, wherein providing the estimate indicating the jurisdictionally-based set of vehicle parts and the set of labor operations for repairing the damaged vehicle includes providing a jurisdiction-specific identifier of each jurisdictionally-based vehicle part and an indication of a respective set of labor operations corresponding to repairing or replacing the each jurisdictionally-based vehicle part.
8. The method of claim 7, further comprising providing a respective cost estimate for repairing or replacing the each jurisdictionally-based vehicle part.
10. The method of claim 1, further comprising obtaining the set of global identifiers of the initial set of parts corresponding to the damaged vehicle from an image processing system that determined the set of global identifiers by analyzing one or more images of the damaged vehicle.
12. The system of claim 11, wherein a total number of identifiers included in the set of global identifiers is different than a total number of parts included in the jurisdictionally-based set of vehicle parts.
13. The system of claim 11, wherein the one or more processors execute the plurality of routines to cause the system further to filter the listing of possible parts stored in the database based on at least one of: a characteristic of the damaged vehicle, one or more requirements included in the set of jurisdictional requirements, respective levels of complexity of one or more parts, or a frequency of part use within the jurisdiction corresponding to the damaged vehicle.
14. The system of claim 11, wherein the estimate generation routine predicts at least one labor operation included in the set of labor operations based on at least one of: a first part included in the initial set of parts, a second part included in the jurisdictionally-based set of vehicle parts, a particular location of damage on the vehicle, a particular content or type of damage to the vehicle, a particular location of damage on a particular part of the vehicle, a particular severity of damage to the particular part, or at least one requirement of the jurisdiction corresponding to the damaged vehicle.
15. The system of claim 11, wherein the estimate generation routine further predicts whether each jurisdictionally-based vehicle part is to be repaired or replaced, and wherein the predicted set of labor operations corresponds to whether each jurisdictionally-based vehicle part is to be repaired or replaced.
16. The system of claim 15, wherein the estimate generation routine predicts whether each jurisdictionally-based vehicle part is to be repaired or replaced based on at least one of: an availability of the each jurisdictionally-based vehicle part, a cost associated with the each jurisdictionally-based vehicle part, a total cost associated with at least one of repairing or replacing the each jurisdictionally-based vehicle part, an overall cost associated with repairing the damaged vehicle, or a configuration parameter.
18. The system of claim 17, wherein the estimate indicates, for each labor operation included in the set of labor operations, at least one of a respective time interval to perform the each labor operation, a respective cost of the each labor operation, or a respective jurisdictionally-based vehicle part.
19. The system of claim 11, wherein the set of jurisdictional requirements includes one or more jurisdictional requirements corresponding to at least one of: hazardous waste disposal, materials which are illegal to utilize in vehicle repair, emissions, a country of manufacture, an insurance regulation, a tax, or a fee.
20. The system of claim 11, wherein the one or more processors are configured to execute the plurality of routines further to obtain the set of global identifiers of the initial set of parts corresponding to the damaged vehicle from an image processing system that determined the set of global identifiers by analyzing one or more images of the damaged vehicle.
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February 1, 2021
June 6, 2023
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